

# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a = True			,				_a = False			)       ->       Optional[Any]:
												_a      :       Tuple   =						scheduler
												_a      :       List[Any]   =						optimizers if isinstance(_a			,				(list, tuple)			) else [optimizers]
												_a      :       Any   =						split_batches
												_a      :       Dict   =						step_with_optimizer
												_a      :       Tuple   =						GradientState()


							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       int:
												if not self.step_with_optimizer:
																	# No link between scheduler and optimizer -> just step
																	self.scheduler.step(*_a			,				**_a			)
																	return

												# Otherwise, first make sure the optimizer was stepped.
												if not self.gradient_state.sync_gradients:
																	if self.gradient_state.adjust_scheduler:
																						self.scheduler._step_count += 1
																	return

												for opt in self.optimizers:
																	if opt.step_was_skipped:
																						return
												if self.split_batches:
																	# Split batches -> the training dataloader batch size is not changed so one step per training step
																	self.scheduler.step(*_a			,				**_a			)
												else:
																	# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																	# num_processes steps per training step
																	_a      :       Tuple   =						AcceleratorState().num_processes
																	for _ in range(_a			):
																						# Special case when using OneCycle and `drop_last` was not used
																						if hasattr(self.scheduler			,				'''total_steps'''			):
																											if self.scheduler._step_count <= self.scheduler.total_steps:
																																self.scheduler.step(*_a			,				**_a			)
																						else:
																											self.scheduler.step(*_a			,				**_a			)


							def 							__lowercase							(     self			)       ->       int:
												return self.scheduler.get_last_lr()


							def 							__lowercase							(     self			)       ->       Optional[int]:
												return self.scheduler.state_dict()


							def 							__lowercase							(     self			,				_a			)       ->       Dict:
												self.scheduler.load_state_dict(_a			)


							def 							__lowercase							(     self			)       ->       str:
												return self.scheduler.get_lr()




							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       List[str]:
												return self.scheduler.print_lr(*_a			,				**_a			)









"""simple docstring"""




import warnings

from .generation import TFGenerationMixin






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				# warning at import time
				warnings.warn(
				    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
				    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,   UpperCAmelCase__		,   )



